void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
Real one(1);
std::vector<Real> myval(5,0), val(5,0);
myval[0] = RiskMeasure<Real>::val_;
myval[1] = RiskMeasure<Real>::gv_;
myval[2] = dval1_;
myval[3] = dval2_;
myval[4] = dval3_;
sampler.sumAll(&myval[0],&val[0],5);
Real xs2 = xstat_*xstat_;
Real xs3 = xs2*xstat_;
Real v02 = val[0]*val[0];
Real h11 = (val[3]*val[0] - val[2]*val[2])/(v02*xs3) * vstat_;
Real h12 = (val[1]*val[2] - val[4]*val[0])/(v02*xs2);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector1_);
sampler.sumAll(*scaledGradient1_,*dualVector2_);
dualVector1_->axpy(one/xstat_,*dualVector2_);
dualVector1_->scale(one/val[0]);
dualVector2_->zero();
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector2_);
dualVector1_->axpy(vstat_*val[2]/(xs2*v02)-val[1]/(v02*xstat_),*dualVector2_);
dualVector2_->zero();
sampler.sumAll(*scaledGradient2_,*dualVector2_);
dualVector1_->axpy(-vstat_/val[0],*dualVector2_);
(Teuchos::dyn_cast<RiskVector<Real> >(hv)).setVector(*dualVector1_);
(Teuchos::dyn_cast<RiskVector<Real> >(hv)).setStatistic(h11+h12);
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
RiskVector<Real> &hs = Teuchos::dyn_cast<RiskVector<Real> >(hv);
std::vector<Real> var(2);
sampler.sumAll(&vec_[0],&var[0],2);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector_);
hs.setStatistic(var);
hs.setVector(*dualVector_);
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_, ev(0), one(1);
sampler.sumAll(&val,&ev,1);
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector1_);
dualVector1_->scale(one/ev);
(Teuchos::dyn_cast<RiskVector<Real> >(g)).setVector(*dualVector1_);
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
RiskVector<Real> &hs = Teuchos::dyn_cast<RiskVector<Real> >(hv);
Real val = RiskMeasure<Real>::val_, var(0), one(1);
sampler.sumAll(&val,&var,1);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector_);
var *= coeff_/(one-prob_);
hs.setStatistic(var);
hs.setVector(*dualVector_);
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
RiskVector<Real> &gs = Teuchos::dyn_cast<RiskVector<Real> >(g);
Real val = RiskMeasure<Real>::val_, var(0), one(1);
sampler.sumAll(&val,&var,1);
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector_);
var *= -coeff_/(one-prob_);
var += coeff_;
gs.setStatistic(var);
gs.setVector(*dualVector_);
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_;
Real dev = 0.0;
sampler.sumAll(&val,&dev,1);
std::vector<Real> pval_sum(NumMoments_);
sampler.sumAll(&(pval_)[0],&pval_sum[0],NumMoments_);
for ( uint p = 0; p < NumMoments_; p++ ) {
dev += coeff_[p] * std::pow(pval_sum[p],1.0/order_[p]);
}
return dev;
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
Real half(0.5);
RiskVector<Real> &gs = Teuchos::dyn_cast<RiskVector<Real> >(g);
std::vector<Real> var(2);
sampler.sumAll(&vec_[0],&var[0],2);
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector_);
var[0] += half*coeff_;
var[1] += half*coeff_;
gs.setStatistic(var);
gs.setVector(*dualVector_);
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
RiskVector<Real> &gs = Teuchos::dyn_cast<RiskVector<Real> >(g);
std::vector<Real> var(size_);
sampler.sumAll(&vec_[0],&var[0],size_);
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector_);
for (int i = 0; i < size_; i++) {
var[i] += coeff_[i];
}
gs.setStatistic(var);
gs.setVector(*dualVector_);
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
RiskVector<Real> &hs = Teuchos::dyn_cast<RiskVector<Real> >(hv);
std::vector<Real> var(size_);
sampler.sumAll(&vec_[0],&var[0],size_);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector_);
// for (int i = 0; i < size_; i++) {
// var[i] *= coeff_[i]/(1.0-prob_[i]);
// }
hs.setStatistic(var);
hs.setVector(*dualVector_);
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector1_);
std::vector<Real> pval_sum(NumMoments_);
sampler.sumAll(&(pval_)[0],&pval_sum[0],NumMoments_);
Teuchos::RCP<Vector<Real> > pg;
for ( uint p = 0; p < NumMoments_; p++ ) {
if ( pval_sum[p] > 0.0 ) {
pg = (pg_[p])->clone();
sampler.sumAll(*(pg_[p]),*pg);
dualVector1_->axpy(coeff_[p]/std::pow(pval_sum[p],1.0-1.0/order_[p]),*pg);
}
}
// Set RiskVector
(Teuchos::dyn_cast<RiskVector<Real> >(g)).setVector(*dualVector1_);
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
Real one(1);
// Perform sum over batches
std::vector<Real> myval(2,0), val(2,0);
myval[0] = RiskMeasure<Real>::val_;
myval[1] = RiskMeasure<Real>::gv_;
sampler.sumAll(&myval[0],&val[0],2);
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector1_);
// Compute partial derivatives
Real gstat = std::log(myval[0]) - myval[1]/(myval[0]*xstat_);
dualVector1_->scale(one/myval[0]);
// Set partial derivatives in g vector
(Teuchos::dyn_cast<RiskVector<Real> >(g)).setVector(*dualVector1_);
(Teuchos::dyn_cast<RiskVector<Real> >(g)).setStatistic(gstat);
}
void getGradient(Vector<Real> &g, SampleGenerator<Real> &sampler) {
RiskVector<Real> &gs = Teuchos::dyn_cast<RiskVector<Real> >(g);
std::vector<Real> mygval(3,0.0), gval(3,0.0);
mygval[0] = RiskMeasure<Real>::val_;
mygval[1] = valLam_;
mygval[2] = valMu_;
sampler.sumAll(&mygval[0],&gval[0],3);
std::vector<Real> stat(2,0.0);
stat[0] = thresh_ + gval[0] + gval[1];
stat[1] = (Real)1 + gval[2];
gs.setStatistic(stat);
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector_);
gs.setVector(*dualVector_);
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_, cvar(0);
sampler.sumAll(&val,&cvar,1);
for (int i = 0; i < size_; i++) {
cvar += coeff_[i]*xvar_[i];
}
return cvar;
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
Real one(1);
std::vector<Real> myval(2), val(2);
myval[0] = RiskMeasure<Real>::val_;
myval[1] = RiskMeasure<Real>::gv_;
sampler.sumAll(&myval[0],&val[0],2);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector1_);
sampler.sumAll(*scaledGradient_,*dualVector2_);
dualVector1_->axpy(coeff_,*dualVector2_);
dualVector1_->scale(one/val[0]);
dualVector2_->zero();
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector2_);
dualVector1_->axpy(coeff_*val[1]/(val[0]*val[0]),*dualVector2_);
(Teuchos::dyn_cast<RiskVector<Real> >(hv)).setVector(*dualVector1_);
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
RiskVector<Real> &hs = Teuchos::dyn_cast<RiskVector<Real> >(hv);
std::vector<Real> myhval(5,0.0), hval(5,0.0);
myhval[0] = RiskMeasure<Real>::val_;
myhval[1] = valLam_;
myhval[2] = valLam2_;
myhval[3] = valMu_;
myhval[4] = valMu2_;
sampler.sumAll(&myhval[0],&hval[0],5);
std::vector<Real> stat(2,0.0);
stat[0] = (vlam_ * hval[1] + vmu_ * hval[0] + hval[2])/xlam_;
stat[1] = (vlam_ * hval[0] + vmu_ * hval[3] + hval[4])/xlam_;
hs.setStatistic(stat);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector_);
hs.setVector(*dualVector_);
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_;
Real ev = 0.0;
sampler.sumAll(&val,&ev,1);
Real gv = RiskMeasure<Real>::gv_;
Real egv = 0.0;
sampler.sumAll(&gv,&egv,1);
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector1_);
sampler.sumAll(*scaledGradient_,*dualVector2_);
dualVector1_->plus(*dualVector2_);
dualVector1_->scale(1.0/ev);
dualVector2_->zero();
sampler.sumAll(*(RiskMeasure<Real>::g_),*dualVector2_);
dualVector1_->axpy(egv/(ev*ev),*dualVector2_);
(Teuchos::dyn_cast<RiskVector<Real> >(hv)).setVector(*dualVector1_);
}
void getHessVec(Vector<Real> &hv, SampleGenerator<Real> &sampler) {
sampler.sumAll(*(RiskMeasure<Real>::hv_),*dualVector1_);
std::vector<Real> pval_sum(NumMoments_);
sampler.sumAll(&(pval_)[0],&pval_sum[0],NumMoments_);
std::vector<Real> pgv_sum(NumMoments_);
sampler.sumAll(&(pgv_)[0],&pgv_sum[0],NumMoments_);
Real c = 0.0;
for ( uint p = 0; p < NumMoments_; p++ ) {
if ( pval_sum[p] > 0.0 ) {
sampler.sumAll(*(pg_[p]),*dualVector2_);
sampler.sumAll(*(pg0_[p]),*dualVector3_);
sampler.sumAll(*(phv_[p]),*dualVector4_);
c = coeff_[p]*(pgv_sum[p]/std::pow(pval_sum[p],2.0-1.0/order_[p]));
dualVector1_->axpy(c,*dualVector3_);
c = coeff_[p]/std::pow(pval_sum[p],1.0-1.0/order_[p]);
dualVector1_->axpy(c,*dualVector2_);
dualVector1_->axpy(c,*dualVector4_);
}
}
// Set RiskVector
(Teuchos::dyn_cast<RiskVector<Real> >(hv)).setVector(*dualVector1_);
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_, cvar(0), half(0.5);
sampler.sumAll(&val,&cvar,1);
cvar += half*coeff_*(xvar_[0] + xvar_[1]);
return cvar;
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_;
Real ev = 0.0;
sampler.sumAll(&val,&ev,1);
return std::log(ev);
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_, gval = 0.0;
sampler.sumAll(&val,&gval,1);
return xlam_*(thresh_ + gval) + xmu_;
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_, cvar(0);
sampler.sumAll(&val,&cvar,1);
cvar += coeff_*xvar_;
return cvar;
}
Real getValue(SampleGenerator<Real> &sampler) {
Real val = RiskMeasure<Real>::val_, ev(0);
sampler.sumAll(&val,&ev,1);
return xstat_*std::log(ev);
}
void WorldParserObj::CreateObstacle(const std::string& line)
{
if(line.find("c ") == 0)
{
char r[255],g[255],b[255],t[255];
sscanf(line.c_str(),"c %s %s %s %s",r,g,b,t);
//manager_.SetNextColor(strtod (r, NULL), strtod(g, NULL), strtod(b, NULL));
//manager_.SetNextTransparency(strtod (t, NULL));
return;
}
vector<long int> indices;
vector<long int> normalindices;
T_Point points;
for(int i =0; i<1; ++i)
{
string ligne = doubleSlash(line);
ligne=remplacerSlash(ligne); //On remplace les '/' par des espaces, ex : pour "f 1/2/3 4/5/6 7/8/9" on obtiendra "f 1 2 3 4 5 6 7 8 9"
vector<string> termes=splitSpace(ligne.substr(2)); //On éclate la chaîne en ses espaces (le substr permet d'enlever "f ")
int ndonnees=(int)termes.size()/3;
for(int i=0; i <ndonnees;i++) //On aurait très bien pu mettre i<ndonnees mais je veux vraiment limiter à 3 ou 4
{
long int idx = (strtol(termes[i*3].c_str(),NULL, 10)) - 1;
long int idn = (strtol(termes[i*3+2].c_str(),NULL, 10)) - 1;
std::vector<long int>::iterator it = indices.begin();
it = find (indices.begin(), indices.end(), idx);
if(it == indices.end())
{
indices.push_back(idx);
points.push_back(points_[(int)idx]);
}
it = find (normalindices.begin(), normalindices.end(), idn);
if(it == normalindices.end())
{
normalindices.push_back(idn);
}
}
}
Vector3d p1(points[0]);
Vector3d p2(points[1]);
Vector3d p3(points[2]);
Vector3d u_(p3-p1);
Vector3d v_(p2-p1);
if(abs( u_.dot(v_)) > 0.001) { v_ = u_; u_ = p2-p1;}
//we need convex hull of this crap
Vector3d normal (u_.cross(v_));
normals_[(int)normalindices[0]];
SampleGenerator * generator = SampleGenerator::GetInstance();
Obstacle * obstacle;
if(normal.dot(normals_[(int)normalindices[0]]) > 0 )
{
generator->AddObstacle(new Obstacle(p1, p2, p3));
}
else
{
generator->AddObstacle(new Obstacle(p3, p2, p1));
}
}
